Dr. Michael Winter

Arbeit an der ZHAW

Tätigkeit

• Senior Researcher in Fachstelle Process Science
• Dozent für Wirtschaftsinformatik

Arbeits- und Forschungsschwerpunkte

• Digital Health
• Process Intelligence

Lehrtätigkeit

• Geschäftsprozessintegration
• AI in Medicine
• Medical Informatics
• Business Process Management
• Data & Process Mining
• Business Process Intelligence
• Advanced Concepts for Data and Process Management
• Concepts for Data and Process Management

Lehrtätigkeit in der Weiterbildung

CAS Human-AI Collaboration

Berufserfahrung

• Healthcare Data & Process Analyst
  Lenox UG
  2025 - heute
• Data Scientist & Digital Architect
  Freelancer
  2020 - heute
• Digital Health Informatician
  Universitätsklinikum Würzburg
  2022 - 2026
• Learning Experience Designer
  School of Advanced Professional Studies
  2015 - 2017

Netzwerk

ORCID digital identifier

ORCID ID: 0000-0003-2561-7923

Auszeichnungen

Dissertationspreis
Ulmer Universitätsgesellschaft
07 / 2023

Social Media

LinkedIn

Publikationen

Beiträge in wissenschaftlicher Zeitschrift, peer-reviewed

• Winter, M. et al. (2026) 'Safeguarding patient privacy in eHealth systems : bridging theory and practice through a scoping review and healthcare survey', PLOS Digital Health, 5(3), p. e0001325. doi: 10.1371/journal.pdig.0001325.
• Winter, M. et al. (2026) 'Longitudinal mental health data collected via the Corona Health smartphone app during COVID-19', Scientific Data, 13(392).
• Vogel, C. et al. (2025) 'Assessing a visual editor for healthcare questionnaires based on the fast healthcare interoperability resources (FHIR) standard : protocol for a cross-sectional, mixed-methods usability evaluation using eye-tracking and retrospective think-aloud', BMJ Open, 15(9), p. e099744. doi: 10.1136/bmjopen-2025-099744.
• Winter, M. et al. (2025) 'A comparison of self-reported COVID-19 symptoms between android and iOS CoronaCheck app users', npj Digital Medicine, 8(197). doi: 10.1038/s41746-025-01595-1.

Publikationen vor Tätigkeit an der ZHAW

• Zimoch, M., Kolb, J., & Reichert, M. (2016, June). Considering social distance as an influence factor in the process of process modeling. In International Conference on Business Process Modeling, Development and Support (pp. 97-112). Cham: Springer International Publishing.
• Winter, M., Vogel, C., Schobel, J., Schlüter, M., Baumeister, H., Terhorst, Y., ... & Pryss, R. (2026). Longitudinal mental health data collected via the Corona Health smartphone app during COVID-19. Scientific Data.
• Winter, M., Kraft, R., Belas, C., Reichert, M., Ertl, M., & Pryss, R. (2026). Safeguarding patient privacy in eHealth systems: Bridging theory and practice through a scoping review and healthcare survey. PLOS Digital Health, 5(3), e0001325.
• Elsner, C., Winter, M., Gocke, P., Pryss, R., & Krefting, D. (2026). The Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS) Framework: Holistic Approach to Pandemic mHealth Apps. JMIR Formative Research, 10(1), e63920.
• Wilhelm, C., Scheich, M., Winter, M., Allgaier, J., Rippstein, J., Zeissig, S. R., ... & Hagen, R. (2026). Exploring the Adoption of Mobile Health Apps Among Patients with Head and Neck Cancer After the COVID-19 Pandemic: Secondary Analysis of Cross-Sectional Survey. JMIR Cancer, 12, e65192.
• Winter, M., Probst, T., Keil, T., & Pryss, R. (2025). A comparison of self-reported COVID-19 symptoms between android and iOS CoronaCheck app users. NPJ Digital Medicine, 8(1), 197.
• Vogel, C., Pryss, R., Heuschmann, P., Rücker, V., & Winter, M. (2025). Assessing a visual editor for healthcare questionnaires based on the fast healthcare interoperability resources (FHIR) standard: protocol for a cross-sectional, mixed-methods usability evaluation using eye-tracking and retrospective think-aloud. BMJ open, 15(9), e099744.
• Winter, M., Probst, T., John, D., & Pryss, R. (2025). Recognizing and understanding stress in adults during Covid-19: data insights from the corona health app. Data in Brief, 111967.
• Winter, M., Deserno, L., Romanos, M., & Pryss, R. (2025). Mental health and ecological momentary assessments during COVID-19: Data from the corona health app adolescents study. Data in Brief, 60, 111619.
• Pryss, R., & Winter, M. (2025). Künstliche Intelligenz im Gesundheitswesen–Möglichkeiten, Herausforderungen und Potenzial. Die Diabetologie, 21(6), 679-686.
• Möller, M., Winter, M., & Reichert, M. (2025). Cognitive factors in process model comprehension—A systematic literature review. Brain Sciences, 15(5), 505.
• Winter, M., Grimmer, J., Reichert, M., & Pryss, R. (2025). From monochrome to color-Exploring the effects of different colorizations on process model comprehension. Cognitive Systems Research, 101417.
• Winter, M., Kirikkayis, Y., & Pryss, R. (2025, April). A Comparative Evaluation. In Internet Computing and IoT and Embedded Systems, Cyber-physical Systems, and Applications: 25th International Conference, ICOMP 2024, and 22nd International Conference, ESCS 2024, Held as Part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024, Las Vegas, NV, USA, July 22–25, 2024, Revised Selected Papers (p. 98). Springer Nature.
• Winter, M., Langguth, B., Schlee, W., & Pryss, R. (2024). Process mining in mHealth data analysis. npj Digital Medicine, 7(1), 299.
• Schlüter, M., Pryss, R., Schlee, W., Reichert, M., & Winter, M. (2024, December). Exploring the Application of Process Mining on Tinnitus Patient Data. In International Conference on Computational Science and Computational Intelligence (pp. 3-13). Cham: Springer Nature Switzerland.
• Winter, M., Eckel, L., Reichert, M., & Pryss, R. (2024, December). Development of a Mobile-Based Gaze-Tracker for Enhanced Survey Responses: Integrating Eye Gaze as a Weight Factor in Answer Evaluation. In International Conference on Computational Science and Computational Intelligence (pp. 3-14). Cham: Springer Nature Switzerland.
• Kirikkayis, Y., Gallik, F., Winter, M., & Reichert, M. (2024). Improving comprehensibility of IoT-driven business processes through bpmn 2.0 extension: A user study. Procedia Computer Science, 239, 19-26.
• Baß, J., Winter, M., Pryss, R., & Reichert, M. (2024). Exploring comprehension strategies of modular process models: A combined eye-tracking and concurrent think-aloud study. Brain Sciences, 14(4), 303.
• Edler, J. S., Winter, M., Steinmetz, H., Cohrdes, C., Baumeister, H., & Pryss, R. (2024). Predicting depressive symptoms using GPS-based regional data in Germany with the CORONA HEALTH app during the COVID-19 pandemic: Cross-sectional study. Interactive Journal of Medical Research, 13(1), e53248.
• Winter, M., Schlee, W., Probst, T., Stach, M., & Pryss, R. (2023, December). Follow-up evaluation to explore disparities between android and ios users utilizing the trackyourtinnitus mobile health platform. In 2023 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1493-1497). IEEE.
• Breitmayer, M., Arnold, L., Winter, M., & Reichert, M. (2024). Exploring user editing preferences in electronic forms: an empirical study: M. Breitmayer et al. Information Systems and e-Business Management, 22(1), 171-207.
• Winter, M., Probst, T., Tallon, M., Schobel, J., & Pryss, R. (2024). The operationalization of cognitive systems in the comprehension of visual structures. Frontiers in Neuroscience, 18, 1501636.
• Winter, M., Kirikkayis, Y., Pryss, R., & Reichert, M. (2024, July). Advancing IoT process modeling: A comparative evaluation of bpmne4iot and traditional bpmn on user-friendliness, effectiveness, and workload. In World Congress in Computer Science, Computer Engineering & Applied Computing (pp. 98-107). Cham: Springer Nature Switzerland.
• Winter, M., & Pryss, R. (2024). The effects of modular process models on gaze patterns-A follow-up investigation about modularization in process model literacy. Expert Systems with Applications, 237, 121176.
• Baß, J., Winter, M., Pryss, R., & Reichert, M. (2024). Exploring comprehension strategies of modular process models: A combined eye-tracking and concurrent think-aloud study. Brain Sciences, 14(4), 303.
• Kirikkayis, Y., Winter, M., & Reichert, M. (2024). A user study on modeling IoT-aware processes with BPMN 2.0. Information, 15(4), 229.
• Winter, M., Kirikkayis, Y., Pryss, R., & Reichert, M. (2024, July). Understanding User Interactions with IoT Process Models: A Demographic Perspective. In World Congress in Computer Science, Computer Engineering & Applied Computing (pp. 88-97). Cham: Springer Nature Switzerland.
• Kirikkayis, Y., Gallik, F., Winter, M., & Reichert, M. (2023). BPMNE4IoT: a framework for modeling, executing and monitoring IoT-driven processes. Future Internet, 15(3), 90.
• Winter, M., Bredemeyer, C., Reichert, M., Neumann, H., & Pryss, R. (2023). A comparative cross-sectional study on process model comprehension driven by eye tracking and electrodermal activity.
• Winter, M., Neumann, H., Pryss, R., Probst, T., & Reichert, M. (2023). Defining gaze patterns for process model literacy–exploring visual routines in process models with diverse mappings. Expert Systems with Applications, 213, 119217.
• Winter, M., & Pryss, R. (2023, October). An empirical exploration of working memory, selective attention and reasoning during the comprehension of process models. In 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 2081-2086). IEEE.
• Idrees, A. R., Kraft, R., Winter, M., Küchler, A. M., Baumeister, H., Reilly, R., ... & Pryss, R. (2023). Exploring the usability of an internet-based intervention and its providing eHealth platform in an eye-tracking study. Journal of Ambient Intelligence and Humanized Computing, 14(7), 9621-9636.
• Humer, E., Keil, T., Stupp, C., Schlee, W., Wildner, M., Heuschmann, P., ... & Pryss, R. (2023). Associations of country-specific and sociodemographic factors with self-reported covid-19–related symptoms: Multivariable analysis of data from the coronacheck mobile health platform. JMIR public health and surveillance, 9(1), e40958.
• Winter, M., Pryss, R., Probst, T., & Reichert, M. (2021). Applying Eye Movement Modeling Examples to Guide Novices’ Attention in the Comprehension of Process Models. Brain Sci. 2021, 11, 72.
• Winter, M., Pryss, R., Fink, M., & Reichert, M. (2021). Towards Measuring and Quantifying the Comprehensibility of Process Models--The Process Model Comprehension Framework. arXiv preprint arXiv:2106.12880.
• Winter, M., Bredemeyer, C., Reichert, M., Neumann, H., Probst, T., & Pryss, R. (2021, June). How healthcare professionals comprehend process models-an empirical eye tracking analysis. In 2021 ieee 34th international symposium on computer-based medical systems (cbms) (pp. 313-318). IEEE.
• Winter, M., Pryss, R., Probst, T., & Reichert, M. (2021). Applying eye movement modeling examples to guide novices’ attention in the comprehension of process models. Brain sciences, 11(1), 72.
• Gallik, F., Winter, M., Kirikkayis, Y., Pryss, R., & Reichert, M. (2021, October). Dyvpromo-a lightweight web-based tool for the dynamic visualization of additional information in business process models. In 2021 IEEE 25th International Enterprise Distributed Object Computing Workshop (EDOCW) (pp. 345-348). IEEE.
• Winter, M., Baumeister, H., Frick, U., Tallon, M., Reichert, M., & Pryss, R. (2021, November). Exploring the usability of the german covid-19 contact tracing app in a combined eye tracking and retrospective think aloud study. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 2215-2221). IEEE.
• Winter, M., Pryss, R., Probst, T., Schlee, W., Tallon, M., Frick, U., & Reichert, M. (2021). Are Non-Experts Able to Comprehend Business Process Models--Study Insights Involving Novices and Experts. arXiv preprint arXiv:2107.02030.
• Dode, A., Mehdi, M., Pryss, R., Schlee, W., Probst, T., Reichert, M., ... & Winter, M. (2021). Using a visual analog scale (VAS) to measure tinnitus-related distress and loudness: investigating correlations using the Mini-TQ results of participants from the TrackYourTinnitus platform. Progress in Brain Research, 263, 171-190.
• Winter, M., Pryss, R., Probst, T., & Reichert, M. (2020). Towards the applicability of measuring the electrodermal activity in the context of process model comprehension: Feasibility study. Sensors, 20(16), 4561.
• Winter, M., Pryss, R., Probst, T., Baß, J., & Reichert, M. (2020). Measuring the cognitive complexity in the comprehension of modular process models. IEEE Transactions on Cognitive and Developmental Systems, 14(1), 164-180.
• Hoppenstedt, B., Probst, T., Reichert, M., Schlee, W., Kammerer, K., Spiliopoulou, M., ... & Pryss, R. (2020). Evaluating usability aspects of a mixed reality solution for immersive analytics in industry 4.0 scenarios. JoVE (Journal of Visualized Experiments), (164), e61349.
• Winter, M., Pryss, R., Probst, T., & Reichert, M. (2020). Learning to read by learning to write: Evaluation of a serious game to foster business process model comprehension. JMIR Serious Games, 8(1), e15374.
• Zimoch, M., Pryss¹, R., Probst, T., & Schlee, W. (2019, January). The Repercussions of Business Process Modeling Notations on Mental Load. In Business Process Management Workshops: BPM 2018 International Workshops, Sydney, NSW, Australia, September 9-14, 2018, Revised Papers (p. 133). Springer.
• Hoppenstedt, B., Probst, T., Reichert, M., Schlee, W., Kammerer, K., Spiliopoulou, M., ... & Pryss, R. (2019). Applicability of immersive analytics in mixed reality: Usability study. IEEE Access, 7, 71921-71932.
• Winter, M., Pryss, R., & Reichert, M. (2019, October). ProMoEE-A lightweight web editor supporting study research on process models. In International Conference on Service-Oriented Computing (pp. 289-293). Cham: Springer International Publishing.
• Tallon, M., Winter, M., Pryss, R., Rakoczy, K., Reichert, M., Greenlee, M. W., & Frick, U. (2019). Comprehension of business process models: Insight into cognitive strategies via eye tracking. Expert Systems with Applications, 136, 145-158.
• Zimoch, M., Pryss, R., Layher, G., Neumann, H., Probst, T., Schlee, W., & Reichert, M. (2018, June). Utilizing the capabilities offered by eye-tracking to foster novices’ comprehension of business process models. In International Conference on Cognitive Computing (pp. 155-163). Cham: Springer International Publishing.
• Andrews, K., Zimoch, M., Reichert, M., Tallon, M., Frick, U., & Pryss, R. (2018). A smart mobile assessment tool for collecting data in large-scale educational studies. Procedia computer science, 134, 67-74.
• Zimoch, M., Pryss, R., Probst, T., Schlee, W., & Reichert, M. (2017, May). Cognitive insights into business process model comprehension: Preliminary results for experienced and inexperienced individuals. In International Workshop on Business Process Modeling, Development and Support (pp. 137-152). Cham: Springer International Publishing.
• Zimoch, M., Pryss, R., Probst, T., Schlee, W., Layher, G., Neumann, H., & Reichert, M. (2017). Evaluating the Comprehensibility of Graphical Business Process Models–An Eye Tracking Study.
• Zimoch, M., Mohring, T., Pryss, R., Probst, T., Schlee, W., & Reichert, M. (2017, September). Using insights from cognitive neuroscience to investigate the effects of event-driven process chains on process model comprehension. In International conference on business process management (pp. 446-459). Cham: Springer International Publishing.
• Zimoch, M., Pryss, R., Schobel, J., & Reichert, M. (2017, May). Eye tracking experiments on process model comprehension: lessons learned. In International Workshop on Business Process Modeling, Development and Support (pp. 153-168). Cham: Springer International Publishing.
• Zimoch, M., Pryss, R., Probst, T., Schlee, W., & Reichert, M. (2017, June). Towards a conceptual framework fostering process comprehension in healthcare. In 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS) (pp. 167-168). IEEE.
• Kolb, J., Zimoch, M., Weber, B., & Reichert, M. (2014, March). How social distance of process designers affects the process of process modeling: insights from a controlled experiment. In Proceedings of the 29th Annual ACM Symposium on Applied Computing (pp. 1364-1370).

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